knitr::opts_chunk$set(echo=TRUE)
library(plotly)
library(reshape2)
library(raster)
library(weathermetrics)

GB_auto <- raster::getData('GADM', 
                           country="GBR", 
                           level=0, 
                           #set the path to store your data in
                           path='Practical_4_Data', 
                           download=TRUE)

GBclim <- raster::getData("worldclim", 
                          res=5, 
                          var="tmean",
                          #set the path to store your data in
                          path='Practical_4_Data', 
                          download=TRUE)

month <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", 
           "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")

names(GBclim) <- month
GBtemp <- crop(GBclim, GB_auto)
exactGB <- mask(GBtemp, GB_auto)

#WorldClim data has a scale factor of 10!
exactGB <- exactGB/10

alldf=as.data.frame(exactGB)
squishdata <- melt(alldf, measure.vars=names(alldf))

# split the data for plotly based on month
jan<-subset(squishdata, variable=="Jan", na.rm=TRUE)
jun<-subset(squishdata, variable=="Jun", na.rm=TRUE)

# give axis titles
x <- list (title = "Temperature")
y <- list (title = "Frequency")

# set the bin width
xbinsno<-list(start=-5, end=20, size = 2.5)

# plot the histogram calling all the variables we just set
ihist<-plot_ly(alpha = 0.6) %>%
        add_histogram(x = jan$value,
        xbins=xbinsno, name="January") %>%
        add_histogram(x = jun$value,
        xbins=xbinsno, name="June") %>% 
        layout(barmode = "overlay", xaxis=x, yaxis=y)

ihist

x <- 1 + 1

y <- 2 + 2

z <- x + y